


import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


UpperCamelCase					:       List[str]			    =     argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
UpperCamelCase					:       Any			    =     parser.parse_args()


UpperCamelCase					:       Tuple			    =     """cpu"""
UpperCamelCase					:       int			    =     """a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

UpperCamelCase					:       List[Any]			    =     """path-to-your-trained-model"""
UpperCamelCase					:       Dict			    =     StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
    UpperCamelCase					:       Tuple			    =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
UpperCamelCase					:       Dict			    =     pipe.to(device)

# to channels last
UpperCamelCase					:       Optional[int]			    =     pipe.unet.to(memory_format=torch.channels_last)
UpperCamelCase					:       Union[str, Any]			    =     pipe.vae.to(memory_format=torch.channels_last)
UpperCamelCase					:       str			    =     pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
    UpperCamelCase					:       str			    =     pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
UpperCamelCase					:       str			    =     torch.randn(2, 4, 64, 64)
UpperCamelCase					:       Union[str, Any]			    =     torch.rand(1) * 999
UpperCamelCase					:       Any			    =     torch.randn(2, 77, 768)
UpperCamelCase					:       Tuple			    =     (sample, timestep, encoder_hidden_status)
try:
    UpperCamelCase					:       Union[str, Any]			    =     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
    UpperCamelCase					:       Optional[Any]			    =     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
UpperCamelCase					:       str			    =     ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
UpperCamelCase					:       Tuple			    =     ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
    UpperCamelCase					:       Any			    =     ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
UpperCamelCase					:       Optional[int]			    =     666
UpperCamelCase					:       Any			    =     torch.Generator(device).manual_seed(seed)
UpperCamelCase					:       int			    =     {"""generator""": generator}
if args.steps is not None:
    UpperCamelCase					:       Tuple			    =     args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
    UpperCamelCase					:       str			    =     pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")









"""simple docstring"""





from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						complex						,							_lowerCamelCase			:						str = "x"						,							_lowerCamelCase			:						float = 10**-10						,							_lowerCamelCase			:						int = 1						,							) ->							complex:
  lowerCamelCase_       	=		symbols(_lowerCamelCase					)
  lowerCamelCase_       	=		lambdify(_lowerCamelCase						,							_lowerCamelCase					)
  lowerCamelCase_       	=		lambdify(_lowerCamelCase						,							diff(_lowerCamelCase						,							_lowerCamelCase					)					)

  lowerCamelCase_       	=		starting_point

  while True:
    if diff_function(_lowerCamelCase					) != 0:
      lowerCamelCase_       	=		prev_guess - multiplicity * func(_lowerCamelCase					) / diff_function(
          _lowerCamelCase					)
    else:
      raise ZeroDivisionError('Could not find root'					) from None

    # Precision is checked by comparing the difference of consecutive guesses
    if abs(next_guess - prev_guess					) < precision:
      return next_guess

    lowerCamelCase_       	=		next_guess


# Let's Execute
if __name__ == "__main__":
  # Find root of trigonometric function
  # Find value of pi
  print(F'''The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}''')

  # Find root of polynomial
  # Find fourth Root of 5
  print(F'''The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}''')

  # Find value of e
  print(
      '''The root of log(y) - 1 = 0 is ''',
      F'''{newton_raphson('log(y) - 1', 2, variable='y')}''',
  )

  # Exponential Roots
  print(
      '''The root of exp(x) - 1 = 0 is''',
      F'''{newton_raphson('exp(x) - 1', 10, precision=0.005)}''',
  )

  # Find root of cos(x)
  print(F'''The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}''')




